Identification of lung disease types using convolutional neural network and VGG-16 architecture

Bukhori, S.; Verdy, B. Y. N.; Eka, Y. R. Windi; Januar, A. P.

Identification of lung disease types using convolutional neural network and VGG-16 architecture

Файли

271023-670512-1-10-20231106.pdf (441.42 KB)

Дата

2023

Автори

Видавець

КПІ ім. Ігоря Сікорського

Анотація

Abstract. Pneumonia, tuberculosis, and Covid-19 are different lung diseases but have similar characteristics. One of the reasons for the worsening of disease in lung sufferers is a diagnosis that takes a long time. Another factor, the results of the X-ray photos look blurry and lack contracture, causing different diagnostic results of X-ray photos. This research classifies lung images into four categories: normal lungs, tuberculosis, pneumonia, and Covid-19 using the Convolutional Neural Network method and VGG-16 architecture. The results of the research with models and scenarios without pre-trained use data with a ratio of 9:1 at epoch 50, an accuracy of 94%, while the lowest results are in scenarios using data with a ratio of 8:2 at epoch 50, non-pre-trained models, accuracy by 87%.

Ключові слова

tuberculosis, pneumonia, Covid-19, VGG-16, convolutional neural network, туберкульоз, пневмонія, згорткова нейронна мережа

Бібліографічний опис

Identification of lung disease types using convolutional neural network and VGG-16 architecture / S. Bukhori, B. Y. N. Verdy, Y. R. Windi Eka, A. P. Januar // Системні дослідження та інформаційні технології : міжнародний науково-технічний журнал. – 2023. – № 3. – С. 96-107. – Бібліогр.: 26 назв.